The paper evaluates the out-of-sample predictive potential of machine learning methods in the cross-section of international equity index returns using firm fundamentals and macroeconomic predictors. The relatively small number of equity indices in the cross-section compared to the multitude of...

In this study, we estimate the effect of industry distress on recovery rates by using the unconditional quantile regression (UQR) proposed in Firpo, Fortin, and Lemieux (2009). The UQR provides better interpretative and thus policy-relevant information on the marginal effect of the covariates...

Traditional machine learning methods have been widely studied in financial innovation. My study focuses on the application of deep learning methods on asset pricing.I investigate various deep learning methods for asset pricing, especially for risk premia measurement. All models take the same set...

This paper investigates the predictability of market betas for crypto assets. The market beta is the optimal weight of a short position in a simple two-asset portfolio hedging the market risk. Investors are therefore keen to forecast the market beta accurately. Estimating the market beta is a...

We propose a new model of asset returns with common factors that shift relevant parts of the stock return distributions. We show that shocks to such non-linear common movements in the panel of firm's idiosyncratic quantiles are priced in the cross-section of the US stock returns. Such risk...

In this very first paper of a series of forthcoming research papers of ours, which we propose to bring out in near future regarding the detailed statistical and mathematical analysis of the financial resources and the budget of the Bangalore Municipal Corporation (more appropriately now called...

Tail risk protection is a mantra in portfolio allocation. A common method in this context is the NMFRB allocation. Here, we extend it to drawdown risk measures and show that the proposed portfolios compete with machine learning-based portfolios such as Hierarchical Risk Parity (HRP) and...

We propose a portfolio allocation method based on risk factor budgeting using convex Nonnegative Matrix Factorization (NMF). Unlike classical factor analysis, PCA, or ICA, NMF ensures positive factor loadings to obtain interpretable long-only portfolios. As the NMF factors represent separate...

I develop a method to identify the strongest determinants of expected returns among potentially infinite return predictors. Instead of sorting stocks on characteristics, I sort stocks into portfolios based on their realized returns---the variable of interest---at each month in the past and find...

We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional bag-of-words approach, the contextualized representation captures both the syntax and semantics of text, thus...